THE POOR, THE PROSPEROUS AND THE INBETWEENERS : A FRESH PERSPECTIVE ON GLOBAL SOCIETY, INEQUALITY AND GROWTH

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1 THE POOR, THE PROSPEROUS AND THE INBETWEENERS : A FRESH PERSPECTIVE ON GLOBAL SOCIETY, INEQUALITY AND GROWTH Peter Edward and Andy Sumner 1 10 June 2013 DRAFT FOR COMMENT Abstract: What has happened to between and within country inequality since 1990? In this paper we explore who have been the winners and losers from global growth since We find that in the last 30 years falls in total global inequality are predominantly attributable to rising prosperity in China. We also identify a persistent global structure of two relatively homogeneous clusters (the poor/insecure and secure/prosperous). We detect the emergence of a new global middle but question whether this implies the end of the historical two cluster world rather than merely a transition as some people move from the poor/insecure cluster into the secure/prosperous cluster. Nevertheless, we do identify five different stylized patterns of national growth: pro-poor growth (eg Ethiopia); pro-middle growth (eg Brazil); anti-poor growth (eg Nigeria); anti-middle growth (eg Zambia) and equitable growth (eg Vietnam). We also find that 15% of growth from 1990 to 2010 went to the world s richest 1% while just a modest amount of redistribution would have ended $2 poverty. If the share of global growth flowing to those who were $2 a day poor in 2010 had increased from 5% to just 12% this would have been sufficient to end $2 poverty today. Persistence of global poverty, it seems, is not due to insufficient global growth but to a reluctance among the secure/prosperous cluster to forego a small share of their benefits from global growth in favour of fairly modest redistribution to the global poor. Key Words: Poverty; Inequality; Economic Development JEL Codes: D63; I32 1 Respectively, Newcastle University Business School and King s International Development Institute, King s College London. Correspondence to peter.edward@ncl.ac.uk and andrew.sumner@kcl.ac.uk Many thanks for important comments to Simon Maxwell, Owen Barder, Richard Manning, Martin Evans, Borge Wietzke, Stephanie Seguino. 1

2 EXECUTIVE SUMMARY In this paper we provide new estimates of the evolution of inequality between and within countries and explore who have been the winners and losers from global growth since We find that total global inequality was relatively static from the late 1980 s to 2005 with rising within country inequality largely offset by falling between country inequality. Since 2005 between country inequality has been falling more quickly than before and as a result total global inequality has also fallen. We find in the last 20 to 30 years falls in total global inequality, and in global between country inequality, and rises in global within country inequality are all predominantly attributable to rising prosperity in China. The picture looks rather different when China is excluded. Throughout this entire period within country inequality in the rest of the world has overall been remarkably constant as some countries have become less equal others have become more so while between country inequality has actually increased slightly. We identify a persistent global structure of two relatively homogeneous clusters (the poor/insecure and secure/prosperous) but we also detect the emergence of a rapidly changing and heterogeneous new global middle. However, most of the structural change in the distribution of global consumption is confined to the upper middle income countries (UMICs). This leads us to question whether the emerging global middle really does represent an evolution away from the historical two cluster world or whether it simply represents a transition phase as some elements in emerging economies move from the poor/insecure cluster into the secure/prosperous cluster. Nevertheless, we do identify five different stylized patterns of national growth as follows: pro-poor growth (eg Ethiopia); pro-middle growth (eg Brazil); anti-poor growth (eg Nigeria); anti-middle growth (eg Zambia) and equitable growth (eg Vietnam). We also find that 15% of global consumption growth from 1990 to 2010 went to the richest 1% of global population. At the other end of the distribution, the 53% under $2 in 1990 benefitted from less than an eighth of that global growth; and the 37% on less than $1.25 a day benefitted by little more than a twentieth of that growth. A modest amount of redistribution would have ended $2 poverty - if the share of global growth since 1990 flowing to the 35% of the global population who are $2 poor in 2010 had increased from 5% to 12% this would have been sufficient to end 2

3 poverty at $2. In short, the persistence of global poverty seems to have little to do with there being insufficient global growth and a lot more to do with a lack of collective will among the secure/prosperous cluster to forego a small share of their benefits from global growth in favour of a fairly modest amount of redistribution to the global poor. 3

4 Contents 1. Introduction 2. Review of studies 3. The GrIP (Growth, Inequality and Poverty) model 4. What has happened to between and within country inequality since 1990? 5. Inequality in a multi-layered world 6. Conclusion Annex 4

5 1. INTRODUCTION The interplay of between and within country inequality, the relative contribution of each to overall global inequality and the implications this has for who benefits (and by how much) from recent global growth has become a significant avenue for research and analysis. 2 Indeed, a number of recent empirical papers have discussed changes in inequality, some with reference to class in the contemporary period of globalisation, typically considered to be since the collapse of the Soviet Union and the end of the Cold War in the late 1980s (some on inequality e.g. Milanovic, 2011; 2012a; Palma, 2011; Ravallion and Chen, 2012 and others on the emergence of new middle classes Kharas, 2010; Ravallion, 2010). In this paper we provide new estimates of the evolution of inequality between and within countries and then focus on inequality in a multi-layered world. These are new in the sense they are derived from a purpose built model. We use globallystandardised absolute consumption thresholds to identify and consider the fortunes of four global layers as follows: the global absolute poor (<$2 a day); the global prosperous (>$50 a day) and those in between, specifically, the global insecure ($2 to $10 a day) and the global secure ($10 to $50 a day). We prefer to call these global layers, rather than classes, because the conflation of differences in per capita consumption levels with class is - of course problematic since class is a social and political identity that does not automatically follow, even at the national level let alone the global level, from consumption (or income) level (see discussion in Sumner, 2012). We derived estimates using the GrIP ( Gr owth, I nequality and P overty) model (version 1.0), which has been developed from an earlier model described in Edward (2006) and is discussed briefly below and in more depth in Edward and Sumner (2013). The paper itself is structured as follows: Section 2 reviews recent empirical studies. Section 3 outlines the GrIP model. Section 4 asks what has happened to between and within country inequality over the last 20 years. Section 5 then presents the layers approach to inequality and estimates of the distribution of benefits during the period of globalisation since the end of the Cold War up to Section 6 concludes. 2 Elsewhere one could note the innovations in inequality research related to The World Top Incomes database. See: 5

6 2. REVIEW OF STUDIES 2a. Points of departure A review of peer-reviewed studies published between 2000-present relating to the empirical study of long-run trends in income/consumption inequality identified more than 70 relevant papers in economics and development journals. 3 A parallel review of the distribution of the benefits of economic growth produced more than 50 further papers. 4 These papers point towards a high level of interest and concern in the ways that growth, inequality and poverty interact. However, because of the 2008 PPP revisions (for 2005 PPPs), many of these studies have had a relatively short shelflife. Several recent papers are worthy of note because they are especially relevant to the analysis later in this paper. These papers deal with class (defined in various ways), geography (meaning location) and changes in inequality since 1990 in order to sustain arguments about changes in inequality during the contemporary period of globalization and the distribution of the benefits of growth during that period. It is these papers that in part inspired the current paper s investigation. These papers are (in no particular order): Milanovic (2011; 2012a), Palma (2011) and Ravallion and Chen (2012). Later we also note those papers focusing on the middle classes such as Kharas (2010) and Ravallion (2010) amongst others. It is noteworthy that these papers, and the literature more generally, show a discernible shift away from reliance on single-figure measures of inequality, such as the Gini coefficient, and towards greater use of fractile (quintiles/deciles/ventiles etc) data to explore how economic growth affects individuals at different consumption/income levels both within countries and transnationally. 5 These recent studies noted above have made two arguments in particular: First, that global inequality (defined in different ways) is falling because international (between countries) inequality is falling. Second, that within country 3 The criteria for selection of articles here is (i) empirical in nature; (ii) long-run in nature meaning they span periods of at least 5 years; (iii) based on cross-country analysis (rather than studies of one or a small number of countries) and (iv) published since 2000 (a suitable cut off because of the improvements in inequality data that emerged at the end of the 1990s. In the text we only review studies since the 2008 update of PPPs (for 2005) because the PPP revision was substantial. 4 The criteria were the same as above footnote. 5 The Gini over emphasises how income is distributed and changes around the middle of the distribution. See discussion in Cobham and Sumner (2013). 6

7 inequality is rising in fast growing Asia, albeit from relatively low levels, and is falling in Latin America, albeit from very high levels. Elsewhere, trends in within country inequality in sub-saharan Africa (SSA) are difficult to discern regionally with clarity. 6 This points towards the issue of the interplay of between and within country inequality and the role of China in particular. 7 As we show below, rapid economic growth in China has been the dominant factor in recent reductions in populationweighted between country inequality and at the same time inequality within China has increased very significantly influencing within country inequality estimates. One question is thus if China is excluded then from 1990 to 2010 what has happened to between and within country inequality across the rest of the world? 2b. Inequality, Class and Geography The papers noted above approach between and within country inequality in different ways. For example, Palma (2011) focuses predominantly on similarities and differences in within country inequality across the world. Using a WDI dataset that includes observations for 135 countries (ibid, p.89) with information on Gini and income shares, Palma reaches the following conclusions: First, he shows that about 80 per cent of the world s population now lives in regions whose median country has a Gini close to 40, implying that globalisation has reduced regional differences in within country inequality. Second, Palma shows that the high inequality outliers to this tendency are now only located among middle-income and rich countries. In other words, the poor and upwards side of the Kuznets Inverted-U between inequality and income per capita has evaporated - and with it, Palma argues the hypothesis that posits that for poor developing countries inequality has to worsen in the earlier stages of development. 8 6 Studies on Latin America, argue that the changes in inequality are partly policy induced (eg cash transfer programmes) and partly the result of a fortuitous interplay of changes in relative wages for skilled and unskilled workers due to commodity booms creating higher demand for relatively unskilled labour (see Lustig et al., 2011; 2012). 7 Nel (2012), for one, has noted that in much economic literature there is a tendency to disconnect studies of changes in within country inequality from the broader economic integration of countries into contemporary globalization and global production patterns. Many scholars have considered the role of globalization in contributing to within-country inequality. Not least are those who focus on the functional distribution of income. 8 Ravallion and Chen (2012) have noted somewhat similar findings that inequality within growing developing countries falls about as often as it rises... The evidence leads one to doubt that higher inequality is simply the price for higher growth and lower absolute poverty (2012, p. 5). The issue 7

8 Palma s third conclusion, and the one that has received attention, is the startling finding that within a global trend of rising inequality, there are two opposite forces at work. One is centripetal and leads to a growing uniformity in the incomeshare appropriated by the middle 50 per cent (deciles 5 to 9). The other is centrifugal and leads to an increased diversity in the shares of the top 10 per cent (decile 10) and bottom 40 per cent (deciles 1 to 4). The share of the national consumption/income that accrues to, or is appropriated by, the five middle deciles (deciles 5 to 9) is remarkably constant across countries, with a median value of 52% and most values within the range of 50-55% (Palma, 2011, p. 101 and 102). 9 This, he argues, indicates that, regardless of differences in national per capita income or in political-institutional settlements, half of the world s population (the middle and upper-middle classes) have acquired strong property rights over half of their respective national incomes; the other half, however, is increasingly up for grabs between the very rich and the poor (ibid, p ). The remaining half of the national income is variously distributed between the poorest 40% (deciles 1 to 4) and the richest 10% (decile 10) in each country. There is, however, a wide diversity, between countries, in the share appropriated by the richest 10%. This leads Palma to propose that the problem of national inequality is one of Homogenous Middles, Heterogeneous Tails in which the key issue is the capture of GNI of the richest and the poorest and globalisation is thus creating a distributional scenario in which what really matters is the income-share of the rich (because the rest follows ). 10 with this and other studies is that it is not really a question of whether growth improves or worsens inequality. It is what specific macroeconomic policies have what effect. For example, some types of macro policy are equity enhancing and some are not and there are examples of both profit led and wage led growth. 9 Cobham and Sumner (2013) corroborate and explore Palma s (2011) findings. 10 One interesting observation from this is that a country s Gini coefficient is therefore predominantly dependent on the income share of the richest decile. Palma notes that the Gini coefficient can be (roughly) estimated as 1.5 times the share of the top 10 per cent (in percentage points) minus 15, which implies (if one assumes that the share of the lowest 40% is negligible and that inequality among Palma s middle class is also negligible that the maximum likely Gini would be in the region of 60 percent (2011, p. 103). Notably, Palma identifies two middle-income regions Southern Africa and Latin America where countries typically have Gini indices between 55 and 60, in other words very close to the maximum likely Gini value. In practice a few Gini indices slightly higher than 60 can be found (e.g. Seychelles 66, Comoros 64, Namibia 64, South Africa 63, Micronesia 61, Botswana 61). 8

9 Palma points to three issues: First, the homogenous middle outlined above. Second, the fact that while most regions and countries have generally similar levels of inequality, two middle-income regions (Southern Africa and Latin America) have remarkably high levels of inequality representing what probably amount to the most extreme practicable divisions between the rich, median and poor segments of society (since 60 is the maximum likely Gini value one might speculate that while more extreme divisions are theoretically possible they are likely to prove difficult to sustain consensually as functioning social systems since they imply such a wide level of difference between the top and bottom). Third, it is among the richest countries that the highest diversity of distributions occurs (QED). High levels of development can (at least currently) co-exist with either high or low levels of inequality. Milanovic (2011; 2012a) in contrast, uses data (2011, p. 7) derived from the World Income Distribution database 11 to combine within country inequality distributions with between country inequality measures (mean per capita consumption in real PPP $, based to 2005) to construct a model of inequality between all individuals in the world. Milanovic calls this inequality 3 and outlines as follows: 12 Inequality 3 is [the] global inequality, which is the most important concept for those interested in the world as composed of individuals, not nations. Unlike Palma also explores whether there are any statistical relationships between inequality and mean national income (GDP) per capita, finding that most low and middle income countries/regions have ginis of around 40 whereas it is mainly among the rich countries that the greatest distributional diversity is found - ranging from a Gini of about 46 in the USA down to Ginis close to 25 in Japan and the Nordic countries (Data from WDI). Of course Gini indices higher than 60 are possible when we look at global or regional inequality because this adds in the effect of between country inequality. 11 The dataset is publically available on 12 Inequality 1 is, focused on inequality between nations of the world. It is an inequality statistic calculated across GDPs or mean incomes obtained from household surveys of all countries in the world, without population-weighting (Milanovic, 2012a, p.3). In short, inequality 1 is a measure of between country inequality in which all countries carry equal weight. Inequality 2 is also a between country inequality measure, but it is population weighted. Milanovic argues that the mother of all inequality disputes is due to Inequality 1 and 2 moving in different directions between 1950 and 2010 with Inequality 1 (not population weighted) rising and Inequality 2 (population weighted) falling (2012a, p. 6). As Milanovic notes it is the vastly improved coverage of global data (both national distributions surveys and international PPP comparator data) since the late 1980 s that has enabled analysts to move to truly global (i.e. Inequality 3) modeling, and in doing so to start to consider in some detail how and where growth and inequality interactions lead to winners and losers in the global economy. By the early 2000 s coverage of the data meant that some initial models of true global inequality started to emerge. In essence, all of these can be understood as Inequality 3 types of models, albeit often with not insignificant differences in assumptions and results, notably from how they modeled disaggregated national distributions and how they assigned relative weightings when aggregating country-data into a global picture. Examples of these include, Bhalla (2002), Dikhanov & Ward (2002), Edward (2006), Milanovic (2002; 2005), and Sala-i-Martin (2002). 9

10 the first two concepts, this one is individual-based: each person, regardless of her country, enters in the calculation with her actual income. And Milanovic s (2012a) central argument derived from this Inequality 3 concept is as follows: We see something that may be historically important: perhaps for the first time since the Industrial Revolution, there may be a decline in global inequality. Between 2002 and 2008, global Gini decreased by 1.4 points [i]t is indeed among the very top of the global income distribution and among the emerging global middle class, which includes more than a third of world population, that we find most significant increases in per capita income. The top 1% has seen its real income rise by more than 60% over those two decades. The largest increases however were registered around the median: 80% real increase at the median itself and some 70% around it. It is there, between the 50th and 60th percentile of the global income distribution that we find some 200 million Chinese, 90 million Indians, and about 30 million people each from Indonesia, Brazil and Egypt. These two groups the global 1% and the middle classes of the emerging market economies are indeed the main winners of globalization So who lost between 1988 and 2008? Mostly people in Africa, some in Latin America and post-communist countries (2012a, p. 7, 12, 15). Milanovic (2012a, p. 18) also reprises Milanovic (2011) and decomposes global inequality between class ( differences in incomes within nations ) and location ( differences between mean incomes of all the countries in the world ) in 1870 and 2000 and notes, What is less obvious and less well known is that the shares of the two factors determining global inequality have changed in a remarkable fashion... Around 1870, class explained more than 2/3 of global inequality. And now? The proportions have exactly flipped: more than 2/3 of total inequality is due to location. (2012a, p. 19). 10

11 In short, Milanovic argues that global inequality has become much more about location rather than class. Milanovic s use of the term class is however rather problematic. In general he, like Palma, sees class as a national issue (as measured by within country inequality) but he is also happy to refer to an emerging global middle class that is defined by reference to location in the global income (Inequality 3) 13, 14 distribution. Although as indicated above, we would suggest class may not be a useful term to adopt when looking at global income distributions, Milanovic highlights the benefits of disaggregating the ( Inequality 3 ) global income distribution to look at how different segments of the global population (delineated either in absolute terms or relative to the global distribution) have fared during the last two decades. 15 Finally, Ravallion and Chen (2012) provide a further analysis of between and within country inequality by applying the mean-log deviation (MLD) inequality measure to the PovcalNet database (of more than 850 household surveys from 127 developing countries) to explore changes in within- and between country inequality from 1980 to The MLD is the difference between the log of the group s mean consumption and the mean of the logs of all the consumptions within that group (ibid., p. 2). Unlike the more commonly used Gini coefficient, the MLD can be decomposed by population sub-groups so that the population-weighted MLD provides an estimate of the contribution of within county inequality to overall inequality. Ravallion and Chen (2012, p. 2) note, We see that there has been a trend decrease in total inequality, though with ups and downs, and an increase over However, that pattern has largely been due to inequality between countries. Over the period as a whole, we see that [the within country component] has risen. This is also evident if one 13 From Milanovic s Figure 4 (2012, p. 13), this class appears to be the global population between the 25 th and 60 th percentiles. 14 We note that one issue with Inequality 3 models (and this includes GrIP) is that it is often national level policies that matter in alleviating inequality so that identifying those countries where inequality is rising or falling may be as relevant or more so than attention to global inequality trends across individuals. We address differences in national inequality trends at the end of this paper. 15 While Milanovic does not define explicitly the global middle class, Ravallion (2010) does do so by per capita expenditures (as does Kharas, 2010). He also notes that there is little agreement over what these limits should be: Milanovic and Yitzhaki (2002) defined it as the set of people living between the mean incomes of Brazil and Italy. Instead Banerjee and Duflo (2008) defined the middle class as those living between $2 and $10 a day at 1993 PPP. Bhalla (2007)... proposed a lower cut-off point of... about $10 per day in 2005 [PPP]... he set an upper bound at 10 times his lower one. (Ravallion, 2010, p. 446) 11

12 takes out China, which has a high weight, and also saw a large increase in inequality The within-country component accounted for less than one third of inequality in the developing world as a whole in 1981, but almost half in In short, whereas Milanovic suggests that from 1870 to 2000 within country inequality ( class ) became less important and between country inequality ( geography ) more important, Ravallion and Chen (2012) find the opposite trend in recent decades, namely that since the early 1980 s within country inequality has become an increasingly important element in overall global inequality. 16 It seems possible therefore that the long-run trend of the 20 th Century towards lower inequality within countries but higher inequality between countries could be starting to reverse with the extremes between rich and poor people increasing within countries while the income gaps between rich countries and poor countries start to reduce. However, caution is needed here because (as we show later) these global differences can be largely attributed to a few large emerging economies which disguise that large differences between the remaining countries remain rather intractable (for example, when China is removed from the analysis we find that between country inequality has risen since 1980 while within country inequality hardly changed). 16 Ravallion and Chen also (2012, p. 4) note significant variations between regions. For example, the region with the highest average inequality within countries is Latin America and the Caribbean (LAC) in which 90% of inequality is within countries, although this has been falling since The secondhighest average within country inequality is in sub-saharan Africa (SSA). And South Asia and East Asia both have generally had low within country inequality but this has been rising since the 1990s. 12

13 3. THE GrIP (GROWTH, INEQUALITY AND POVERTY) MODEL In this paper we use a model of growth, inequality and poverty the GrIP model, which has been developed from an earlier model described in Edward (2006). The GrIP model in its current iteration is described in detail in Edward and Sumner (2013). 17 The main objective of the GrIP model is to construct a truly global model of or consumption distribution that allows ready comparison of different assumptions. The model is built by combining consumption (or income) distribution data derived from national surveys with internationally comparable measures of mean national consumption. The core approach in the GrIP model is to take for each country the distribution (quintile and decile) data and combine this with data on national population and on the mean consumption per capita in internationally comparable PPP $. The model uses this input to develop for each country an estimate of how many people live at any specific consumption ($-a-day) level. This is very similar to the approach in Povcal except that: GrIP uses a linear rather than kernel distribution function; draws on a wider range of sources to extend the analysis to include developed economies (including allowing estimation for countries where some of the source data is missing); and allows the use of a variety of possible sources for the mean per capita consumption (whereas Povcal relies only on survey means which can limit the ability to extend the scope to include other countries). Having identified for each country the number of people living at a given consumption level, GrIP then aggregates these to build a truly global income distribution (of course a wide variety of other aggregations are also readily produced for example by region or income category as shown in the various results presented here). These aggregations can then be interrogated to investigate issues such as poverty levels, trends in inequality and who are the winners and losers from global growth. Survey distributions (quintile and upper and lower decile data) are available from a number of sources although the most commonly used source in the World Bank s PovcalNet. 18 In GrIP, distribution data is taken (in this order of preference) from PovcalNet, World Development Indicators or the UNU WIID V2.0c (May 2008) 17 See: Version04March2013.pdf 18 In this paper we use Povcal version of February,

14 database. 19 National consumption means can come from survey data or from National Account (NA) measures. The GrIP model enables us to make analyses using either survey means (Option 1 in GrIP) or NA means (Option 2). Survey means are taken from PovcalNet while NA means are taken from World Development Indicators. All analysis and results are in 2005 PPP $. There are a number of methodological issues to consider and acknowledge. First, even though these datasets have greatly improved their global coverage in recent years, there are still some significant gaps in the data so that in order to construct a truly global distribution it remains necessary to decide how to deal with missing data. Surveys do not take place annually so in the GrIP model distributions for intermediate years between surveys are calculated by interpolation while in years subsequent to the most recent survey the distribution is assumed to remain unchanged from that survey. This still leaves situations where a country has no surveys or the gaps between surveys are too great to allow reliable interpolation. In these cases the GrIP model can estimate, or fill, a country s missing distributions with the (not population-weighted) average distribution from all other countries in the same region and income group (i.e. in the GrIP model the analysis can either be filled to include these estimates and hence build a model that more closely replicates global population and consumption totals, or not filled which means that the analysis only includes the smaller set of countries for which national distribution data is available and hence covers a smaller proportion of global totals). 20 The GrIP model also, and unlike other models, disaggregates the national populations into globally standard $ per capita brackets, thereby avoiding introducing the distortions of approaches, such as Bhalla s simple accounting procedure (Bhalla, 2002; Hillebrand, 2008), where by disaggregating only to percentiles some large step-change distortions are introduced in the later global aggregation at points where percentiles from the very largest countries (such as India 19 See Where WIID V2.0c is used consumption distributions are used in preference to income distributions. In accordance with established practice, no attempts are made to modify income distributions to convert them to consumption distributions on the basis that such conversions are too speculative to be justified. 20 We note also that the distribution data can be derived at either the individual level or the household level. This is an outcome of the original survey design and so is difficult to adjust for in subsequent analysis. As is the case for most other analysts we do not attempt to adjust for this difference but note that household surveys will inevitably understate national inequality to some extent as they do not include intra-household inequality. 14

15 and China where each percentile currently includes well over 10 million people) are added back into the global distribution. GrIP is designed so that the difference between using NA means or survey means can be readily investigated. Survey means are used, for example, in Povcal but other authors tend to use NA means. One reason for this is that the NA means allow one bring together distributions from a wider range of sources. By using the NA means as an internationally standard metric across which to aggregate and compare the individual countries it becomes possible to incorporate countries where distribution data is available but comparable survey means are not available. It also means that informed estimates can be made for inequality in countries where distribution data is absent and so build a more truly global model. A difficulty arises because although in theory survey means should show a consistent relationship to NA means, in practice this is not the case. A general systematic relationship (the NA/Survey ratio) can be estimated but individual countries can show very wide variation from this estimate. Use of survey or NA means can lead therefore to a very different geography of global poverty and inequality so it becomes relevant and useful to compare how the selection of the different means can affect results of the analysis. When we use NA means we simply apply the relevant mean to the survey distribution (this is termed NA/S Option 2 on the various graphs that follow). When we use survey means (Option 1) we apply the survey mean to the distribution where such a mean exists. Where there is no survey mean we adjust the NA mean for the country in question in line with estimates for the systematic difference between the two types of mean (the NA/Survey ratio NA means are in general higher than survey means). Because of this adjustment in Option 2 we also adjust by the same ratio the various thresholds between layers that we identify later. For a more in depth discussion of these issues see Edward and Sumner (2013). In this paper we use Household Final Consumption (HFC) per capita means (in 2005 PPP $) as our NA mean data. Because coverage of GDP data is generally better than that of HFC data, where GDP data exists but HFC data does not then the missing HFC figure is estimated from the GDP data. Wherever possible this is done in a given year by applying the most recent HFC/GDP ratio for the country in question. Where no such ratio exists then the average ratio calculated for all countries with suitable data in the same region and income category is used. Table 1 illustrates how by first estimating missing HFC data from GDP data (for countries that otherwise 15

16 have valid survey distributions) and then using filling to estimate distributions for countries without valid surveys, the GrIP model incrementally builds a global model of inequality from the available source data. It can be clearly seen that the number of countries underpinning the model, and hence also the reliability of any outputs from the model, reduces rapidly once we go back into the 1980s. For this reason the results given here mainly focus on the period from 1990 to Where we do take analysis back into the 1980s those results should be treated with circumspection. Table 1: Coverage (cov.) of analysis and effects of estimating HFC and filling distributions Source data coverage Consumption cov. (%) After estimating missing HFC Consumption cov. (%) After filling missing distributions Consumption cov. (%) No. of Pop n No. of Pop n No. of Pop n Year countries cov. (%) countries cov. (%) countries cov. (%) Source: GrIP v1.0. Percentages are of global totals. 4. WHAT HAS HAPPENED TO BETWEEN AND WITHIN COUNTRY INEQUALITY SINCE 1990? 4a. Global inequality and decompositions Two commonly used measures for global inequality are the Gini index and the Theil index. Of these, Gini is the more widely used, largely because of its close and relatively intuitive association with the Lorenz curve. However Gini suffers from the problem that it is not decomposable so that it becomes difficult to differentiate the separate contributions of within and between country inequality to overall global inequality. In essence, in a truly global model of inequality, changes in the global Gini coefficient arise from three causes: a) changes in between country inequality (Milanovic s population-weighted Inequality 2 ); b) changes in within country inequality; and c) the interaction of (a) and (b). 21 It is therefore difficult to know whether to ascribe the interaction, or overlap, element (c) to between or within country changes. But this element can be significant, especially in situations where 21 In the GrIP model, between country inequality is calculated by assuming there is no within country inequality (i.e. all members of a national population are deemed to have the same consumption per capita). Within country inequality is derived by assuming that all countries have the same average income (so that the only differences in consumption arise in the model from intra-national inequality). 16

17 highly populated countries are experiencing rapid changes both in aggregate national consumption and in within country inequality (as has been the case recently with the large emerging BRIC economies). The Theil index is however fully decomposable, but as a measure of entropy it is rather less intuitive but importantly, is generally more sensitive to changes at the extreme ends of the Lorenz curve. Gini and Theil both give us measures of global inequality but their different sensitivities mean that they are not directly comparable. For this reason we provide estimates here using both indices for the period from 1980 to 2010 (noting again caveats for the 1980s). We also include for comparison estimates from Milanovic (2012b, p. 14) based on survey means. Many other earlier estimates of Gini and Theil exist but they are not directly comparable to our analysis because, as Milanovic shows, the recent rebasing of international PPP rates as a result of the new 2005 ICP revision (relative to the old 1993-based PPP rates) has systematically increased global inequality indices. Gini and Theil results are shown in Figures 1 to 6 (all these coefficients are based on not filled analysis so that they are not distorted by any estimations for missing countries in the filling process). We also include for the Theil index, graphs showing how within country inequality, as a proportion of total global inequality, has changed. For all these figures we show results based on survey means (Option 1) and on NA means (Option 2). We can see from the Gini figures that global inequality has certainly declined since the late 1980s but the amounts are relatively small. Perhaps it is more accurate to say that global inequality by the Gini method was relatively constant through the 1990 s (the variability in the figures prior to the early 2000s looks to be random scatter within the margin of error, which would also encompass Milanovic s estimates, rather than an overall trend). Since the early 2000 s however, global inequality has been falling. The causes of this are interesting because across the entire period within country inequality has been rising slowly but steadily. In the 1990s this rise was approximately balanced out by a gradual fall in between country inequality. Since the early 2000s, the rise in within country inequality has continued at a similar steady rate but between country inequality has started to fall more quickly (although that does not guarantee that these falls will continue into the future) so that overall global inequality has started to fall. 17

18 The Theil index gives a broadly similar conclusion. Its greater sensitivity to changes at the extremes of the distribution means that the survey mean based results might be read as indicating that global inequality was rising slowly in the 1990s rather than broadly static but that effect is not robust enough to show up also when NA means are used. In sum, taken as a whole the results indicate that for the first ten to fifteen years of the period of globalization since the late 1980s, global inequality was relatively static with a slow but steady rise in within country inequality being broadly offset by a gradual decline in between country inequality. Since then, and particularly since 2005, while within country inequality has continued to rise steadily, between country inequality has fallen quite rapidly, and with it global inequality has started to fall too. The interaction of these effects means that whereas in 1988 within country inequality accounted for around 20 to 25% of global inequality by 2010 it had risen to 30% of global inequality. The figures below are consistent with Ravallion and Chen (2012) and seem to be the reverse of the longer term trend Milanovic identifies since 1870 (2012a, p. 18). It is possible that our model is detecting the first signs that the world is trending back towards the situation in the past where countries are more equal relative to each other but more unequal within themselves. 22 While one should be cautious about relying too heavily on the data from the 1980s, interestingly it indicates that the rise in within country inequality started in the late 1980 s (around the time of the fall of the Berlin Wall and the ascendancy of market liberalism) and has continued steadily since then. Between-country inequality however was fairly static until the early 2000s when it started to fall quite quickly. As a result overall global inequality seems to have risen from the early 1980s to the early 2000s but has been falling quite sharply since then. However the picture looks rather different when China is excluded (Figures 7 to 9). In the rest of the world within country inequality has overall been remarkably constant as some countries have become less equal others have become more so. But between country inequality rose steadily in the 1980 s and 1990 s. The rise is particularly noticeable in the Theil coefficient which might indicate that a lot of the rise was caused by increasing inequality between the richest and poorest countries 22 Milanovic estimates that in 1870 the global Theil Index was about 65 with two-thirds of global inequality being due to within country inequality so the world still has a long way to go before we get back to that situation 18

19 (since Theil is more sensitive than Gini to changes at the ends of the distribution). Since 2000 between country inequality has been fairly static (when China is excluded) but there is little sign that a reversal has set in following the developed world s financial crisis in Perhaps it is anyway too early to detect the effects of that crisis in the data but on the basis of what we see here it would seem that recent falls in global inequality are predominantly attributable to rising prosperity in China. Elsewhere a trend since 1980 of increasing inequality between rich countries and poor countries may have stalled since 2000 but it is not apparent that it has gone into decline even after the 2008 financial crisis. This should give us pause for thought before celebrating too keenly recent and very modest signs of falling overall global inequality. The rapid progress of China may be masking underlying trends that are rather less progressive. 19

20 Figure 1: Global Gini coefficient, survey means (not filled) Global, Opt1 Between-ctry, Opt1 Within-ctry, Opt1 Milanovic Figure 2: Global Gini coefficient, NA means (not filled) Global, Opt2 Between-ctry, Opt2 Within-ctry, Opt2 Figure 3: Global Theil Index, survey means (not filled) 120 Figure 4: Global Theil Index, NA means (not filled) Global, Opt1 Between-ctry, Opt1 Within-ctry, Opt1 Milanovic Global, Opt2 Between-ctry, Opt2 Within-ctry, Opt2 Figure 5: Within country Theil component as percentage of global Theil Index, survey means (not filled) 35 Figure 6: Within country Theil component as percentage of global Theil Index, NA means (not filled) Within as % of global, Opt Within as % of global, Opt2 20

21 Figure 7: World excl. China, Gini coefficient, survey means (not filled) Global excl. China, Opt 1 Between-ctry Within-ctry Figure 8: World excl. China, Theil Index, survey means (not filled) Global excl. China, Opt 1 Between-ctry Within-ctry Figure 9: World excl. China, Within country Theil component as percentage of global Theil Index, survey means (not filled) Within as % all, excludes China 21

22 4b. Within country Ginis by regional and country groups One way to look at within country inequality further is to look at how it has changed. What do the data say? Figures show respectively Gini coefficients by regional and country groups and selected individual countries. All are survey means (option 1) and not-filled. Regional groupings do not include High Income countries (HICs) within that region. 23 As Palma (2011) found, Latin America and sub-saharan Africa are the most unequal regions in the world. As others have noticed, however, in Latin America since 2000 inequality has been falling, reversing a trend of rising inequality in the 1990s. This is largely the result of falling within country inequality in the region. While Brazil has been a significant contributor to this fall it has not been confined to Brazil with inequality falling across the rest of the region also since 2000 (See Annex Table A1). However, it remains to be seen whether this trend will continue or whether it represents merely the latest phase in the cyclical fluctuation of the regional Gini around a value of 50 to 55. At first sight, sub-saharan Africa s fluctuations seem to be cyclical also. However there are some significant differences. Within country inequality has fallen across the region as a whole since 1990 despite a significant and continuing increase in South Africa. When South Africa is removed from the analysis the picture for the rest of SSA is one of relatively constant overall inequality as rising between country inequality is offset by falling within country inequality. In East Asia, overall inequality has risen sharply since the late 1980s, driven not surprisingly by a rise in within country inequality that is largely down to changes in China. When China is removed from the analysis within country inequality in the region is found to have risen only slightly since the mid-1980s. Furthermore, to put some context on this, China s inequality appears even now to be both slightly lower and growing less rapidly than the USA. By all measures, South Asia remains one of the lowest inequality regions (which might not be a good thing if this merely reflects high levels of absolute poverty) but there are signs that inequality there may be starting to increase. 23 EAP = East Asia and Pacific; LAC = Latin America and Caribbean; SAR = South Asia Region; SSA = sub-saharan Africa. High Inc = All High Income countries in the world. 22

23 Figure 10: Gini coefficients (between- plus within country inequality) by region 60 Figure 11: Gini-coefficient for sub-saharan Africa, excluding South Africa EAP LAC SAR SSA High Inc Figure 12: Gini coefficients (within country inequality only) by region All Between Within Figure 13: USA and EU USA EU, Total EAP LAC SAR SSA High Inc Figure 14: Gini coefficients for selected countries China Brazil India S Africa 23

24 5. INEQUALITY IN A MULTI-LAYERED WORLD 5a. The layers of global society In this section we consider a different angle, focusing on inequality in a multi-layered world. In short, we consider the fortunes of four global layers as follows by per capita consumption: the global absolute poor ; the global prosperous and those in between, specifically, the global insecure and the global secure. One conclusion to draw from the inequality indices is that by reducing the highly complex nature of global inequality to a single coefficient it becomes difficult to then take a nuanced view of how global growth interacts with changing national and international inequality. Who have been the winners and losers, for example, from the period of global economic expansion that has taken place since the fall of the Berlin Wall in the late 1980 s? These issues become rather lost when we focus on simple indices and on individual countries. A true global inequality picture (such as Milanovic s Inequality-3 ) can however allow us to move beyond these limitations and to develop a properly transnational view of changing global inequality. One approach has been used for several decades now to estimate global dollar-a-day poverty levels but those analyses focus only on the poorer countries and only on the lowest income levels (numbers below a global absolute poverty line). The potential of models such as GrIP is that if they incorporate survey data that covers all countries (and not just the poorer countries) then one can start to look at consumption levels and categories as a global phenomenon. Ravallion starts to do this, although only for developing world countries, by defining the middle class as the population segment between a lower-bound absolute threshold of $2 a day and an upper bound threshold of $13 a day (the US poverty line) (all in 2005 PPP $). By contrast, Palma and Milanovic tend to understand class as a relative, national issue. Palma defines the middle class as those between the 40 th and 90 th percentiles. Milanovic does not define classes but writes of within country inequality as derived from class whereas between country inequality is derived from location. In contrast. Kharas has defined the middle class against absolute thresholds with a lower bound of $10 and an upper bound of $100 (Kharas, 2010). 24

25 This paper uses absolute thresholds to identify a multi-layered global society, effectively taking an approach which has an established history from the use of global poverty lines and extending it to cover the full range of global consumption levels. However, as noted above we dislike the use of the term class here and instead propose to call these divisions consumption layers. Our reasons are that class itself is a social and political identity not necessarily linked to estimates of expenditures per capita. While there have been some attempts in sociology to identify global classes these have largely been limited in recent years to considering whether there is now a very small but distinct class of transnationally-oriented elites grounded in globalized circuits of accumulation (Robinson, 2012). However, even in this model this transnational class still competes with nationally oriented classes, which include both local elites and other popular and working classes with strong national identities. And even these theories, which see a very specific and limited scope for the notion of global classes, are strongly disputed (Carroll, 2012). We therefore find the notion of transnational classes to be unhelpful as a basis for trying to identify alignments in consumption levels between different segments of global society Segmentation by absolute consumption levels, as we use here, is limited to the extent that we are grouping people globally by their consumption levels the kind of lifestyles they lead rather than by any deeper class -derived alignment of sociopolitical orientation. The precedent for segmentation by consumption level lies less in class theory than in preference similarity theory the idea that people with similar purchasing power levels tend, wherever they are in the world, to have broadly similar consumption preferences (Linder, 1961). Class is of course most often discussed in terms of types of assets and productive processes, labour markets, and occupational resources (see review in Torche & López-Calva, 2011). Some contemporary sociological analysis of class also places a particular emphasis on economic security (see, for discussion, Erikson & Goldthorpe, 2008; Goldthorpe & McKnight, 2006; Standing, 2011). When we extend these largely economic categorisations transnationally the basis for any alignment becomes all the more one of similar material, rather than cultural or political, interests. Collectively therefore these studies provide justification for using absolute levels of consumption to segment global society on the basis of economic security (vulnerability to poverty) at lower levels of consumption and of broad similarity of 25

26 consumption preferences at higher levels of society but they provide little justification for describing these segments as global classes that is, as united by a shared socio-political identity. 24 In this paper we propose, instead of class, four consumption levels or layers of global society: the global absolute poor ($0-$2 per capita per day); the global insecure ($2-$10); the global secure ($10-$50); and the global prosperous ($50+). We therefore identify three thresholds between these layers, namely $2, $10 and $50 divisions (all in terms of survey means). Identifying such benchmark thresholds is inevitably a rather rough and ready exercise but since we are applying them to a global consumption distribution it makes sense to derive them in relation to the patterns of that distribution. By looking at consumption distribution on a truly global level we can shed some insight onto appropriate segmentation thresholds by identifying where there are clusterings of people with similar consumption levels. We identify, from clusterings of consumption levels, a basis for setting such segment thresholds. Imagine a group of people spread around the world but all with broadly similar income per capita in PPP terms (ie. similar spending or consumption power) then we might think of that as a distinct global cluster. If such a group existed then it would be clustered (presumably with some sort of vaguely normal distribution) around an average income point. In a plot of the number of people at each income level we might therefore expect to see a local peak forming with some sort of bell curve centred on this peak. Furthermore, for such a group of people the closer their incomes are (i.e. the less inequality there is within that cluster) the smaller will be the standard deviation of the bell curve (so that the curve will become taller and narrower). And if the distribution is normal then one standard deviation from the mean would identify the threshold beyond which 15% of the distribution would lie. This 85/15 division makes a useful criterion for selecting thresholds. Consumption density curves from the GrIP model are this sort of plot. In Figure 15 we present the density curve for the world in In the graph 24 As alluded to in the earlier discussion, a body of empirical studies related to developing countries has emerged in response to the growing data on the in-between groups, often referred to as the nonpoor/non-rich, or the non-polar groups or classes. Typically referring to these as the middle classes, more often than not the segmentation is defined by reference to daily expenditure per capita. Many of these recent studies are based on absolute definitions of expenditure per capita/day (PPP), ranging from $2/day to $100/day (see review of Sumner, 2012). Some have taken a relative approach by defining either the literal middle of the income/expenditure distribution in terms of the middle three expenditure quintiles, or the non-literal middle as those between the poor (taken as the bottom 40%) and the rich (taken as the top 10%) (eg Palma, 2011). 26

27 consumption per capita (2005 $ PPP) is plotted on a log scale on the horizontal axis. The vertical dashed line (at $730 pa) represents the $2 a day consumption level. The solid line population curve plotted above the horizontal axis represents the number of people living at each consumption level. The area bounded by this 2010 population curve and the horizontal axis represents the entire global population in That segment of this area that lies to the left of the $2 line represents the proportion of the 2010 population who were living on less than $2 a day (so the ratio of that segment to the entire area of the population curve is the 2010 $2 poverty rate as a percentage of global population). The vertical density axis is dimensionless (for the statisticians it is normalised so that the entire area bounded by the population curve and the horizontal axis aggregates to unity). 25 The lower curves (plotted negatively) work in the same way but they represent the consumption of the people living at any given level of consumption (as shown on the horizontal axis). The area between the consumption curve and the horizontal axis indicates how much the corresponding population (as indicated by the population curve) collectively consumes per annum (in 2005 PPP $). All the curves are normalized to the global total (population or consumption respectively) in the most recent year of analysis (always 2010 in this paper). So, when we plot other population curves in this paper their areas are all relative to and in proportion to the 2010 global population curve. Similarly consumption curves are all relative to the 2010 global consumption curve (option 1 and option 2 graphs are relative to their respective 2010 curves). 25 In theory it would be possible to assign a value in terms of actual population count to this axis but that would also require us to specify a bandwidth along the horizontal axis over which that aggregation was calculated. Since this is a log-scale that bandwidth would not translate readily into a simple concept such as X thousand people per dollar of consumption. It would, we fear, be too readily misunderstood and misquoted so we prefer to present these curves in the dimensionless form used it. That approach also allows us to present the population and consumption curves in one graph on the same scale. 27

28 <= Consumption Density Population => Figure 15: Global density curve, survey means, filled Population on less than $2 a day Population curve Filled: Yes Horizontal axis ,000 10, ,000 1,000, Consumption -0.2 of population -0.3 on less than $2 a day Income ($ PPP per capita pa) - log scale Consumption curve 2010 $2 a day In short, the upper curves show how many people live at each consumption level and the lower curves show how much those people collectively consume. Density curves for the global population are given below (figures 16-19). On these plots we show our three proposed thresholds and also the World Bank s current extreme poverty line ($1.25 a day). These plots are included mainly to show that the basis for our thresholds is broadly robust to changes in modeling assumptions (use of NA or survey means). Here, the filled analysis is utilized so that the curves represent similar proportions of global population, in this case they cover between 96% and 97.5% of the entire global population (see earlier discussion and Table 1). In these curves two main broad peaks can be identified. One is seen in the population curve at low income levels while the other, less well defined one, is best seen in the consumption curve. The peaks are more clearly seen in earlier years but when China is excluded can also been seen to persist into the period. Each of these peaks can be understood as representing the centre of a clustering of individuals, as described above. Our segmentation approach approximately divides each of these clusters into an upper and lower segment, and identifies a dividing point between the two clusters. 28

29 <= Consumption Density Population => <= Consumption Density Population => Figure 16: Global density curve, survey means, filled Filled: Yes ,000 10, ,000 1,000, $1.25 a day $2 a day $10 a day $50 a day Income ($ PPP per capita pa) - log scale Figure 17: Global density curve, NA means, filled Filled: Yes NA/S option: ,000 10, ,000 1,000, $1.25 a day $2 a day $10 a day $50 a day Income ($ PPP per capita pa) - log scale 29

30 <= Consumption Density Population => <= Consumption Density Population => Figure 18: Global density curve, survey means, filled excluding China Filled: Yes ,000 10, ,000 1,000, $1.25 a day $2 a day $10 a day $50 a day Income ($ PPP per capita pa) - log scale Figure 19: Global density curve, NA means, filled excluding China Filled: Yes NA/S option: ,000 10, ,000 1,000, $1.25 a day $2 a day $10 a day $50 a day Income ($ PPP per capita pa) - log scale 30

31 We therefore derive our four global consumption layers, or segments, as follows: First, the global absolute poor we define this as living under the $2 per capita level. This is not only reasonably close to the midpoint of the low-income peak. It is also well-established as the World Bank moderate international poverty line, which is close to the median poverty line across all developing countries ($2.36 pc in 2008) as well as the regional mean poverty line in sub-saharan Africa (SSA) and the South Asia Region (SAR) and China, collectively where many of the world s poor live (Ravallion, 2012, p. 25). The global mean for developing country poverty lines is $4.64 pc which is rather higher than the median because poverty lines can be around $11-$12 pc the mean in Latin America and the Caribbean and in Eastern Europe and close to $4 the mean in East Asia and Pacific (Ravallion, 2012, p. 25). In short, $2 pc seems reasonable because it is close both to the global median and to the mean poverty line in the countries where most of the world s poor live (sub-saharan Africa, South Asia Region and China). It is certainly more appropriate than the extreme $1.25 a day poverty line which, as can be seen from the density curves, currently falls consistently below any central point of the low income peak of the population curve. Second and third, the global insecure and global secure layers meaning respectively $2-$10 per capita and $10-$50 per capita. In the density curves these represent the upper half of the lower income population peak and the lower half of the higher-income consumption peak respectively. Setting the threshold between these at $10 a day represents a reasonable cut-off point in the overlap between these two peaks. From GrIP 87% of the HIC population lives above $10 pc a day which fits our 85/15 rule. 98% of LIC and LMIC populations are below this level. This threshold therefore broadly separates those living rich-world lifestyles from those living developing world lifestyles. Given that there is an inevitable degree of arbitrariness in the precise location of these thresholds the $10 level seems a reasonable point of separation. If we wished to exactly balance out the LIC/HIC separation we would need a threshold of $7, which would give 94% of HIC population above the threshold and 94% of LIC and LMIC population below it. Although any line is arbitrary to some extent, there are reasons not to go any lower than $10 since $10 has been identified as an approximate security from poverty line. An interesting study in Chile, Mexico and Brazil suggest that the risk of falling into poverty was as low as approximately 10% at an initial income of $10/day per capita in all three countries but fell to zero in Chile and Mexico at an initial income close to 31

32 $20/day. The authors refer to this as a vulnerability approach to identifying the middle classes (López-Calva & Ortiz-Juarez, 2011). And Birdsall et al., (2013) noted that $10 is the mean per capita income of those who have completed secondary school across Latin America suggesting such completion of schooling can be associated some kind of greater security. Ravallion (2010) used an even higher threshold, the US poverty line of $13 pc. We therefore propose the $10 threshold and that those living below this level might be referred to as the global insecure segment while those above it would be the global secure. Fourth, the global prosperous meaning those living on above $50/day per capita. This approximates to the mid-point of the higher income peak (so that around half of HIC consumption is by people living above this level and half is below it). It is also the level below which 87% of HIC population live (so fits our 85/15 rule ). A further reason for choosing this location as the division between the global secure and the global prosperous is that it neatly coincides with a depression in the consumption peak. Based on the reasoning above that peaks (and subsidiary peaks) represent clusters of individuals with similar preferences, this depression might be understood as the dividing point between two different clusterings within the rich world, perhaps representing a division between two relatively distinct standards of living. Extending this reasoning, we did also consider introducing a further threshold for the super-rich which would have separated an emerging very high income peak (above the $120 a day level) from the rest of the global prosperous layer. Certainly within this segment there are indications of increasing differentiation along these lines. However, inspection of the underlying data shows that this peak is strongly driven by inequality in the USA. Furthermore, given that there are very substantial errors and exclusions in the measurement in these surveys of the incomes of the super-rich we decided that separating this peak out would lead to an excessive focus on a trend in the data that is currently not seen across a broad range countries and, more significantly, that probably is far from representative of the true scale of inequality and consumption at these high income levels. Figures show the distribution of global society by the layers outlined (see also Annex Tables A2 and A3). 32

33 Figure 20: Estimates of each layer of global population (millions) by region, 1990 and 2010: HFC, survey means, filled East Asia and Pacific (EAP) South Asia Region (SAR) Sub-saharan Africa (SSA) Latin America and Caribbean (LAC) Middle East and North Africa (MNA) Europe and Central Asia (ECA) North America (NAM) Figure 21: Estimates of each layer of global population (millions) by country income groups, 1990 and 2010: HFC, survey means, filled LICs 2000 LMICs UMICs , Less than $2 1990, $2 to $ , $10 to $ , More than $ , Less than $2 2010, $2 to $ , $10 to $ , More than $50 HICs 33

34 Figure 22: Estimates of each layer of global population (millions) for selected countries, 1990 and 2010: HFC, survey means, filled 1000 China India , Less than $2 1990, $2 to $ , $10 to $ , More than $ , Less than $2 2010, $2 to $ , $10 to $ , More than $50 UMICs less China LMICs less India 5b. Trends in the consumption distribution The global distribution curves show that in the mid 1980 s, with caveats noted earlier, we lived in what was predominantly a twin-peak world (see figure 23). In other words there was a fairly well defined global distinction between a substantial poor peak and a smaller rich peak (to which accrued most of global GDP and consumption). This gives the 1985 density curve a distinct dumbbell shape, indicating the division of world population into two fairly well defined and distinct segments - the old North-South or West-Rest division. Since then the dumbbell has become much less distinct (see figure 24) (best seen as the loss of concavity between the poor and rich peaks in the density curves, presented above and below, so that the dumbbell looks, in 2010, more like a rotated rectangle) The old twin-peaks world was identified by Quah (1996). The likelihood of the trend away from a two-peak world was previously detected in Edward (2006, p. 1677). As noted earlier, the recent consumption density curves also show the emergence of a new peak at the highest consumption levels, i.e. approaching $100,000 PPP pc pa. This was present back in 1985 but seems to be becoming more distinct recently - perhaps indicative of the emergence of a super-rich segment pulling away from the global prosperous. However this phenomenon needs to be treated circumspectly for reasons discussed in the text. 34

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